Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts.

OBJECTIVES: We examine how hospital characteristics relate to clinical and operational artificial intelligence (AI) adoption and implementation stages and characterize AI deserts and spatial clustering patterns to highlight place-based AI access gaps among United States (US) hospitals. MATERIALS AND METHODS: We used the 2024 American Hospital Association Annual Survey data from 2720 hospitals with at least one AI response. We applied logistic regression models to examine the associations between hospital characteristics and AI adoption, local indicators of spatial association to identify local clusters, and distance analyses to locate AI desert hospitals (ie, geographically isolated nonadopters located >50 miles from the nearest AI adopters). RESULTS: We found that system membership, larger bed size, and nurse staffing intensity are positively associated with clinical and operational AI adoption and implementation stages, whereas rural location, for-profit ownership, and physician intensity are negatively associated with them. Teaching status is more strongly associated with clinical AI, while system membership is more positively associated with operational AI. Although 68.0% of hospitals adopted ≥1 clinical AI functionalities and 60.7% adopted ≥1 operational AI functionalities, 12.1% of clinical and 13.2% of operational non-adopters are classified as AI desert hospitals. DISCUSSION: AI deserts reveal regional implementation gaps; sustained diffusion requires building shared regional capacity rather than relying only on hospital-level incentives. Policy implications to emerging divides may include tracking implementation stage, providing domain-specific support, and funding regional partnerships. CONCLUSION: US hospital AI diffusion is uneven and spatially structured. AI deserts mark regional gaps in access to AI-adopting hospitals.